
It distinguishes from previous sub-structure fingerprinting methods as it is more explainable. Commonly used fingerprint such as ECFP [1]} is not explainable due to its hashing nature. Explicit sub-structure fingerprint such as PubChem encoding has on average 100 granular sub-structures for a small molecule where many sub-structures are a subset of other ones, making it intractable to know which sub-structure leads to the outcome. In contrast, FCS drug encoding is capable of giving explicit hints as it decomposes each drug molecule into discrete and moderate size partitions of sub-structures as shown in Sec. REF .

It allows for leveraging the massive unlabelled data for improved sub-structure mining. For example, we use the Uniprot dataset [2]} consists of 560,823 unique protein sequences and the ChEMBL database [3]} which includes 1,870,461 drug SMILES strings. We observe that the quality of the mined sub-structures originates from the massive unlabelled data we used. In small datasets, the occurrences of many useful sub-structures are below the reasonable minimum frequency whereas a large aggregation dataset can successfully identify them with a larger sequences pool. We also show that the encoding has better predictive power when using massive unlabelled data compared to using small datasets, in Sec. REF .

